Pro-ZD：一种用于主动零日威胁缓解的可迁移图神经网络方法
Pro-ZD: A Transferable Graph Neural Network Approach for Proactive Zero-Day Threats Mitigation
AI总结：
Pro-ZD通过图神经网络主动识别高风险网络连接，有效缓解零日威胁。
摘要：
在当今的企业网络环境中，周界防火墙规则和分布式防火墙规则共同管理连接性。为了解决由于流量增加和网络架构多样化带来的挑战，企业采用自动化工具生成防火墙规则和访问策略。然而，有效管理动态生成策略带来的风险，尤其是关键资产暴露问题，仍是一个重大挑战。这种挑战被远程用户、自带设备和云整合等趋势导致的网络结构演变进一步加剧。本文介绍了一种新的图神经网络模型，用于识别加权最短路径。该模型有助于检测网络配置错误和高风险连接路径，这些路径可能被用于零日攻击——即利用未披露漏洞的网络攻击。所提出的Pro-ZD框架采用主动方法，自动微调防火墙规则和访问策略，以解决高风险连接并防止未经授权的访问。实验结果突显了Pro-ZD的鲁棒性和可迁移性，实现了在检测高风险连接方面超过95%的平均准确率。
英文摘要：
In today's enterprise network landscape, the combination of perimeter and distributed firewall rules governs connectivity. To address challenges arising from increased traffic and diverse network architectures, organizations employ automated tools for firewall rule and access policy generation. Yet, effectively managing risks arising from dynamically generated policies, especially concerning critical asset exposure, remains a major challenge. This challenge is amplified by evolving network structures due to trends like remote users, bring-your-own devices, and cloud integration. This paper introduces a novel graph neural network model for identifying weighted shortest paths. The model aids in detecting network misconfigurations and high-risk connectivity paths that threaten critical assets, potentially exploited in zero-day attacks -- cyber-attacks exploiting undisclosed vulnerabilities. The proposed Pro-ZD framework adopts a proactive approach, automatically fine-tuning firewall rules and access policies to address high-risk connections and prevent unauthorized access. Experimental results highlight the robustness and transferability of Pro-ZD, achieving over 95% average accuracy in detecting high-risk connections. \
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
